Software & Data








  Income Distribution
     This code produces Table 5 in "Inference for Parametric Lorenz Curves".
     See here for further information

  Vector Autoregression
       See these Slides for further information

      These can reproduce most of the results in tables of 1-6  of this paper
      This can reproduce the results of this paper

Data Sets

Here we give a long list of data sets on "Global Income and Wealth Distribution"

1- Microdata Based


Type of Data: PovcalNet is an online tool hosted by the World Bank which calculates various poverty and income inequality measures underlying WB’s  WDI (World Development Indicators). Its aim is to cover as many countries and years as possible starting from 1980. It also provides grouped data in the form of population and income shares but the underlying household surveys are not accessible.

Access: The data can be accessed from here


LIS (Luxembourg Income and Wealth Study Database)

Type of Data: LIS is the largest available income database of harmonised microdata collected from about 50 countries in Europe, North America, Latin America, Africa, Asia, and Australasia spanning five decades. The LIS datasets contain variables on market income, public transfers and taxes, household- and person-level characteristics, labour market outcomes, and, in some datasets, expenditures. It is also the only source providing microdata on wealth.

Access: Access to the LIS (and LWS) microdata bases is achieved through a remote-execution system called LISSY. LISSY allows researchers to access data while respecting privacy restrictions required by the countries providing the data.  It permits researchers to submit programs written in SAS, SPSS or Stata, which are quickly processed from remote locations. Aggregated results are reported back to users within a few minutes. One must register to obtain access to LISSY. For students (anywhere) and non-student researchers in certain countries, access to the micro data is free. here


OECD IDD (OECD Income Distribution Data Base)

Type of Data:  IDD reports information on the country members of the OECD, and the Russian Federation. IDD contains information on 70 indicators on the country members of the OECD (plus the Russian Federation) classified in four categories: income levels, inequality, poverty, and population. Each measure is presented for three different population groups: (i) the entire population, (ii) the population of working age (18 to 65) adults, and (iii) those of retirement age (66 and over). The database reports eight inequality measures: the Gini coefficient for the distribution of equivalized household disposable income (post taxes and transfers), the standard error of that Gini coefficient, the Gini coefficient before taxes and transfers, the P90/P10, P90/P50 and P50/P10 income decile ratios, the S80/S20 income quintile share ratio, and the S90/S10 income decile share ratio.

Access: The information can be accessed from here.



Type of Data: CEPALSTAT is the statistical database of the United Nations Economic Commission for Latin America and the Caribbean. CEPALSTAT includes a wide range of data and a variety of economic, sociodemographic, and environmental measures for the region, including inequality and poverty estimates.

Access: The data file can be downloaded from here.



Type of Data: SEDLAC provides statistics on poverty and inequality in Latin America and the Caribbean. It is compiled by a partnership between the Center for Distributional, Labor, and Social Studies at the Universidad Nacional of La Plata (CEDLAS- UNLP) and the World Bank’s Poverty Global Practice.

Access: The data file can be downloaded from here.


WID (World Wealth and Income Data Base)

Type of Data: WTID (World Top Income Data Base) provides top income shares of more than 40 countries often derived from tax records. WID also provides time series of wealth-income ratios, as well as of wealth aggregates. WID aims  to cover the whole income and wealth distributions (not only the top) to produce Distributional National Accounts (DINA) annual estimates of the distribution of income and wealth.

 Access: The data can be accessed from here.

 2- Secondary Data Source

WIID (World Income Inequality Database)

Type of Data: Hosted by UNU-WIDER, WIID provides income inequality indices (the Gini index and quintile shares) for as many countries and years as possible from various sources. Its coverage is more than PovcalNet but it is less harmonized.

Access: The data file can be downloaded from here.


ATG (All The Ginis)

Type of Data: This database provides Gini coefficients retrieved from nine sources in order to create a single “standardized” Gini variable. The nine sources are: LIS, SEDLAC, SILC, ECA, WYD, POVCAL, WIID, CEPAL, INDIE. ATG covers the years 1950 to 2012, with 164 countries included and over 3,000 separate Ginis.

Access: The data file can be accessed from here.


SWIID (Standardized World Income Inequality Database)

Type of Data: SWIID aims to provide income inequality data that seek to maximize comparability while providing the broadest possible coverage of countries and years. It incorporates various types of Gini indices for as many countries and as many years as possible along with estimates of uncertainty around them by using various data sources. It also computes missing Gini’s using multiple imputation technique.

Access: The data can be obtained from here.


Chartbook of Economic Inequality (Atkinson & Morelli)

Type of Data: Provides 25 charts representing income inequality, top income shares, poverty measures, dispersion of earning and top wealth shares for 25 countries covering more than one hundred years. The countries are: Argentina, Brazil, Australia, Canada, Finland, France, Germany, Iceland, India, Indonesia, Italy, Japan, Malaysia, Mauritius, Netherlands, New Zealand, Norway, Portugal, Singapore, South Africa, Spain, Sweden, Switzerland, the UK and the US.

Access:  The figures can be download from here    

3- Others

There are still other data sets (some listed below). Further information will be provided in the future.

 EU-SILC (European Community Household Panel Study)   link

GCIP (Global Consumption and Income Project)                  link

CEQ (Commitment to Equity Database)                                 link

UTIP (University of Texas Inequality Project)                       link

Gini Project                                                                                  link

(HHB) Historical Household Budgets                                      link